
from llama_index.core.indices.property_graph import SimpleLLMPathExtractor
from typing import List

from llama_index.core.agent.workflow import  FunctionAgent
from llama_index.core.vector_stores import SimpleVectorStore
from llama_index.core.schema import  TextNode
from llama_index.core import Settings, SimpleKeywordTableIndex, SummaryIndex, get_response_synthesizer
from llama_index.embeddings.zhipuai import ZhipuAIEmbedding
from llama_index.core.graph_stores import SimplePropertyGraphStore
from llama_index.core.schema import Document
from pydantic import BaseModel

embed_model = ZhipuAIEmbedding(
    model="embedding-2",
    api_key="f387f5e4837d4e4bba6d267682a957c9.PmPiTw8qVlsI2Oi5"
    # With the `embedding-3` class
    # of models, you can specify the size
    # of the embeddings you want returned.
    # dimensions=1024
)
Settings.embed_model=embed_model

from llama_index.llms.deepseek import DeepSeek

llm = DeepSeek(model="deepseek-chat", api_key="sk-605e60a1301040759a821b6b677556fb")
Settings.llm = llm

from typing import Any, List, Dict, Optional, Tuple, Type
from types import TracebackType

from llama_index.core.graph_stores.prompts import DEFAULT_CYPHER_TEMPALTE
from llama_index.core.graph_stores.types import (
    PropertyGraphStore,
    Triplet,
    LabelledNode,
    Relation,
    EntityNode,
    ChunkNode,
)
from llama_index.core.graph_stores.utils import (
    clean_string_values,
    value_sanitize,
    LIST_LIMIT,
)
from llama_index.core.prompts import PromptTemplate
from llama_index.core.vector_stores.types import VectorStoreQuery
import neo4j

username="neo4j",
password="admin,12345",
database="neo4j",
url="neo4j://127.0.0.1:7687", 

dr= neo4j.GraphDatabase.driver(
            url,
            auth=(username, password),
            notifications_min_severity="OFF"
        )

rs=dr.get_server_info()
print(rs)

 
